Frequency spectrograms for biometric keystroke authentication using neural network based classifier

作者：

Highlights：

• This paper deals with a novel frequency based authentication method and a Gauss-Newton based Neural Network classifier.

• The purpose of this research is to provide the foundations of frequency authentication to enhance keystroke authentication protocols.

• We presented short time Fourier transform to analyze the train signal of keystrokes.

• We also analyzed the spectrograms to discriminate various signals.

• EER of the proposed feature extraction and classification method is found as 4.1%.

摘要

•This paper deals with a novel frequency based authentication method and a Gauss-Newton based Neural Network classifier.•The purpose of this research is to provide the foundations of frequency authentication to enhance keystroke authentication protocols.•We presented short time Fourier transform to analyze the train signal of keystrokes.•We also analyzed the spectrograms to discriminate various signals.•EER of the proposed feature extraction and classification method is found as 4.1%.

论文关键词：Biometrics,Spectrogram,Frequency,Keystroke authentication,Short-time Fourier transformation,Gauss-Newton based neural networks

论文评审过程：Received 3 May 2016, Revised 19 September 2016, Accepted 11 November 2016, Available online 16 November 2016, Version of Record 14 December 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.11.006